How Do Community Associations Vary? The Structure of Community Associations in Calgary,
Bibliographic record
Abstract
Summary. The growth in territorial community associations has been one of the major trends in Western cities in recent years, but there are few comprehensive studies of the range of variation of these organisations in a single city. This case study of Calgary, a Canadian city of three-quar-ters of a million people in 1991, addresses this gap in our understanding by showing the variation in the character of all 118 community associations in the city. The city contained 94 local community buildings; 21 with an insured value of over 1m dollars. Yet membership levels were relatively low with a mean of 18.9 per cent. A factor analysis of 11 key variables of the variations in community association characteristics revealed 5 basic sources of differentiation, summarised as: resources; programmes and organisation; age and rental revenue dependency; membership; and salaries. A further factor analysis of the social structural characteristics of the community areas identified nine axes called: economic status; family status; age-participation; early middle age; mobility; immigrant-Charter Group; each European ethnic; central and southern European ethnic; and Dutch ethnic-old housing. In general, there were few strong associations between the two sets of generalised axes, demonstrating that the contextual effects of the variations in community association activity were not strong. The only exceptions were that membership levels had medium positive correlates with economic status, and old age areas had community associations that depended highly on revenues from the rental of community facilities to non-member groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".